Senior Robotics Planning Engineer

Who We Are

Pronto AI is a global leader in commercializing autonomous vehicle (AV) technology, deploying Autonomous Haulage Systems (AHS) that automate operations in mines, quarries, and construction sites worldwide. While much of the industry remains in R&D, we deliver real, production-ready autonomy that is already operating in the field. We are on a mission to make mining operations safer, smarter, and more efficient through cutting-edge technology, and we are building toward becoming the world’s first profitable AV technology company.

What You’Ll Do

We're looking for a Robotics Planning Engineer to develop the high-level autonomy systems that coordinate fleets of autonomous haul trucks in mining environments. You'll work on path planning, multi-vehicle coordination, and dispatch systems that operate at the site level — deciding where trucks go, when they go, and how they interact with each other. Motion planning — A robust stack, from path planning to trajectory optimization, to generate smooth and safe trajectories for 200+ ton trucks to follow.

Coordination planning — Systems to simultaneously coordinate the motion of multiple vehicles with intersecting trajectories to avoid collision and maximize throughput. Fleet planning — Algorithms that dynamically translate the site-wide state, like loading and dumping locations, to actively managed assignments for each truck.

What We’Re Looking For

BS/MS/PhD in Robotics, Computer Science, or related field required 7+ years of professional (non-internship) software development experience Strong foundation in motion planning algorithms Experience with computational geometry (collision detection, polygon operations) Proficiency in Python and NumPy for numerical computing Understanding of vehicle kinematics and nonholonomic constraints Ability to analyze algorithm complexity and optimize for real-time performance

Preferred Qualifications

Experience with multi-agent coordination or scheduling algorithms Familiarity with Dubins/Reeds-Shepp curves for non-holonomic planning Background in trajectory optimization (DCBF, MPC-based planners) Experience with graph algorithms (Dijkstra, heuristic search) Knowledge of GEOS, Shapely or other computational geometry libraries Experience with fleet management or dispatch systems Familiarity with Redis, ZeroMQ, or similar infrastructure Familiarity with modern ML techniques for planning problems Technical Environment Languages: Python (primary), C++ (performance-critical modules) Libraries: NumPy, Shapely, Numba, SciPy Testing: Simulation replay, config-driven scenario testing

Why Join Us

Work on real, production-deployed autonomy. Build technology that directly improves safety, efficiency, and productivity. Tackle complex challenges in demanding, real-world environments.

Be part of a fast-moving team with high ownership and impact. See your work deployed and making a difference in the field. Collaborate closely with experienced engineers and industry operators.

What else you need to know This role is based in our San Francisco office location. As a company driven by innovation and continuous change, close collaboration is essential. We’re constantly reimagining our industry, creating new products, and refining our processes, and we do our best work together.

That’s why all of our office-based teams work onsite, five days a week.